Kanda T, Sasaki R, Masuzaki R, Moriyama M. Artificial intelligence and machine learning could support drug development for hepatitis A virus internal ribosomal entry sites. Artif Intell Gastroenterol 2021; 2(1): 1-9 [DOI: 10.35712/aig.v2.i1.1]
Corresponding Author of This Article
Tatsuo Kanda, MD, PhD, Associate Professor, Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, 30-1 Oyaguchi-kamicho, Itabashi-ku 173-8610, Tokyo, Japan. kanda.tatsuo@nihon-u.ac.jp
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Tatsuo Kanda, Reina Sasaki, Ryota Masuzaki, Mitsuhiko Moriyama, Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Itabashi-ku 173-8610, Tokyo, Japan
Author contributions: Kanda T performed the majority of the writing and prepared the figures and tables; Sasaki R performed data acquisition and writing; Masuzaki R provided input in writing the paper; Moriyama M designed the outline and coordinated the writing of the paper; all authors have read and approve the final manuscript.
Supported byThe Japan Agency for Medical Research and Development, No. JP20fk0210075.
Conflict-of-interest statement: There is no conflict of interest associated with any authors who contributed their efforts to this manuscript.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Tatsuo Kanda, MD, PhD, Associate Professor, Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, 30-1 Oyaguchi-kamicho, Itabashi-ku 173-8610, Tokyo, Japan. kanda.tatsuo@nihon-u.ac.jp
Received: October 15, 2020 Peer-review started: October 15, 2020 First decision: December 17, 2020 Revised: December 29, 2020 Accepted: February 12, 2021 Article in press: February 12, 2021 Published online: February 28, 2021 Processing time: 132 Days and 16.2 Hours
Abstract
Hepatitis A virus (HAV) infection is still an important health issue worldwide. Although several effective HAV vaccines are available, it is difficult to perform universal vaccination in certain countries. Therefore, it may be better to develop antivirals against HAV for the prevention of severe hepatitis A. We found that several drugs potentially inhibit HAV internal ribosomal entry site-dependent translation and HAV replication. Artificial intelligence and machine learning could also support screening of anti-HAV drugs, using drug repositioning and drug rescue approaches.
Core Tip: In certain areas, it is difficult to perform universal hepatitis A virus (HAV) vaccination. We found that several drugs potentially inhibit HAV internal ribosomal entry sites-dependent translation and HAV replication. After the application of machine and deep learning, artificial intelligence identified effective anti-HAV drugs more quickly, using drug repositioning and drug rescue.
